Systems for Laboratory Automation

2009 ◽  
pp. 2-2-9
Author(s):  
BC Wonsiewicz ◽  
AR Storm ◽  
JD Sieber
Keyword(s):  
Laboratory Automation

Lack of Agreement of Chromogenic and Glotting Assays for Factor X and Protein C in Warfarinised Plasma

1991 ◽  
Vol 65 (04) ◽  
pp. 360-363 ◽  
Author(s):  
P Han ◽  
K P Fung ◽  
U Rahdakrishnan
Keyword(s):  
Serine Proteases ◽  
Protein C ◽  
Warfarin Therapy ◽  
Intraclass Correlation ◽  
Chromogenic Substrate ◽  
Factor X ◽  
Laboratory Automation ◽  
Quantitative Results ◽  
Assay Methods

SummaryCoagulation serine proteases can be measured with either a chromogenic substrate assay or a clotting assay using deficient plasmas. It is a concern whether both assays give similar quantitative results, in particular in plasma obtained fiom patients on long term warfarin therapy. If these two assay methods were interchangeable, then the chromogenic substrate assay has the advantages of precision as well as laboratory automation. We used the intraclass correlation coefficient (r1) to assess the agreement between the two methods in measuring factor X and protein C levels in warfarinised plasma. The results indicate that the extent and pattern of agreement of the two methods for the measurement of the two variables in warfarinised plasma are poor, despite high Pearson product moment coefficients of correlation.

scholarly journals Optimal Scheduling for Laboratory Automation of Life Science Experiments with Time Constraints

2021 ◽  
pp. 247263032110217
Author(s):  
Takeshi D. Itoh ◽  
Takaaki Horinouchi ◽  
Hiroki Uchida ◽  
Koichi Takahashi ◽  
Haruka Ozaki
Keyword(s):  
Scheduling Algorithms ◽  
Mixed Integer ◽  
Time Constraints ◽  
Live Cells ◽  
Laboratory Automation ◽  
Simulation Based Design ◽  
Simulation Based ◽  
Optimal Allocations ◽  
Scheduling Method ◽  
Time Required

In automated laboratories consisting of multiple different types of instruments, scheduling algorithms are useful for determining the optimal allocations of instruments to minimize the time required to complete experimental procedures. However, previous studies on scheduling algorithms for laboratory automation have not emphasized the time constraints by mutual boundaries (TCMBs) among operations, which is important in procedures involving live cells or unstable biomolecules. Here, we define the “scheduling for laboratory automation in biology” (S-LAB) problem as a scheduling problem for automated laboratories in which operations with TCMBs are performed by multiple different instruments. We formulate an S-LAB problem as a mixed-integer programming (MIP) problem and propose a scheduling method using the branch-and-bound algorithm. Simulations show that our method can find the optimal schedules of S-LAB problems that minimize overall execution time while satisfying the TCMBs. Furthermore, we propose the use of our scheduling method for the simulation-based design of job definitions and laboratory configurations.

scholarly journals Available options for doing more with less: laboraory automation as one tool in the arsenal

1998 ◽  
Vol 20 (3) ◽  
pp. 87-89
Author(s):  
Stephen Scypinski ◽  
John Baiano ◽  
Theodore Sadlowski
Keyword(s):  
Laboratory Automation ◽  
Drug Discovery Process ◽  
Contract Research Organization ◽  
Advantages And Disadvantages ◽  
Line Extensions ◽  
Use Of Resources ◽  
Analytical Development ◽  
Effective Use ◽  
Case Basis

Projects that require analytical support can evolve from a number of different situations, for example new molecular entities from drug discovery; process changes; packaging changes; site changes; line extensions; and inlicensed projects and compounds. Laboratory automation has been shown to provide a viable and practical solution to assisting in analytical development. However, it is not always the most logical answer. A truly flexible and responsive analytical unit will make a decision on a case-by-case basis, when faced with a new project, whether it is best to: automate some or all aspects/testing involved; contract out to a reputable and approved contract research organization (CRO); hire temporary help; use available in-house resources; use a combination of the options shown above (for example to evaluate the complexity of the new project versus what the in-house resources are currently working on). The paper discusses the advantages and disadvantages of the various options with respect to providing analytical support and suggests optionsfor the most effective use of resources. The role of automation as one of the important tools in the arsenal of these options is highlighted.

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